plot.Predict.Treat.ContCont function

Plots the distribution of the individual causal effect based on $S$.

Plots the distribution of $\Delta T_j$|$S_j$ and the $1-\alpha$% CIs for the mean and median $\rho_{T0T1}$ values (and optionally, for other user-requested $\rho_{T0T1}$ values).

## S3 method for class 'Predict.Treat.ContCont'
plot(x, Xlab, Main, Mean.T0T1=FALSE, Median.T0T1=TRUE,  
Specific.T0T1="none", alpha=0.05, Cex.Legend=1, ...)
## S3 method for class 'Predict.Treat.Multivar.ContCont'
plot(x, Xlab, Main, Mean.T0T1=FALSE, Median.T0T1=TRUE,  
Specific.T0T1="none", alpha=0.05, Cex.Legend=1, ...)

Arguments

• x: An object of class Predict.Treat.ContCont or Predict.Treat.Multivar.ContCont. See Predict.Treat.ContCont or Predict.Treat.Multivar.ContCont.
• Xlab: The legend of the X-axis of the plot. Default "$\Delta T_j$|$S_j$".
• Main: The title of the PCA plot. Default " ".
• Mean.T0T1: Logical. When Mean.T0T1=TRUE, the $1-\alpha$% CI for the mean $\rho_{T0T1}$ value (i.e., the mean of all valid $\rho_{T0T1}$ values in x) is shown. Default FALSE.
• Median.T0T1: Logical. When Median.T0T1=TRUE, the $1-\alpha$% CI for the median $\rho_{T0T1}$ value is shown. Default TRUE.
• Specific.T0T1: Optional. A scalar that specifies a particular value $\rho_{T0T1}$ for which the $1-\alpha$% CI is shown. Default "none".
• alpha: The $\alpha$ level to be used in the computation of the CIs. Default $0.05$.
• Cex.Legend: The size of the legend of the plot. Default $1$.
• ...: Other arguments to be passed to the $plot()$ function.

References

Alonso, A., Van der Elst, W., & Molenberghs, G. (submitted). Validating predictors of therapeutic success: a causal inference approach.

Author(s)

Wim Van der Elst, Ariel Alonso, & Geert Molenberghs

See Also

Predict.Treat.ContCont

Examples

# Generate the vector of PCA.ContCont values when rho_T0S=.3, rho_T1S=.9, 
# sigma_T0T0=2, sigma_T1T1=2,sigma_SS=2, and the grid of values {-1, -.99, 
# ..., 1} is considered for the correlations between T0 and T1:
PCA <- PCA.ContCont(T0S=.3, T1S=.9, T0T0=2, T1T1=2, SS=2, 
T0T1=seq(-1, 1, by=.01))

# Obtain the predicted value T for a patient who scores S = 10, using beta=5,
# SS=2, mu_S=4
Predict <- Predict.Treat.ContCont(x=PCA, S=10, Beta=5, SS=2, mu_S=4)

# examine the results
summary(Predict)

# plot Delta_T_j given S_T and 95% CI  based on 
# the mean value of the valid rho_T0T1 results  
plot(Predict, Mean.T0T1=TRUE, Median.T0T1=FALSE, 
xlim=c(4, 13))

# plot Delta_T_j given S_T and 99% CI  using 
#  rho_T0T1=.8   
plot(Predict, Mean.T0T1=FALSE, Median.T0T1=FALSE, 
Specific.T0T1=.6, alpha=0.01, xlim=c(4, 13))